Optimal configurations for regular-chassis fully-actuated multirotors reduce to N-5 disconnected 1D topological branches corresponding to star polygons {N/q}.
The International Journal of Robotics Research40(8-9), 1015–1044 (2021)
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The paper proposes an STL-based optimization planner with uncertainty-aware risk analysis and event-triggered replanning for safe human-drone collaboration, demonstrated in simulations of an object handover task.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
INDI exhibits stronger robustness than NDI+NDO under model mismatch, external disturbances, and measurement noise in simulation tests of aerial robot control.
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The N-5 Scaling Law: Topological Dimensionality Reduction in the Optimal Design of Fully-actuated Multirotors
Optimal configurations for regular-chassis fully-actuated multirotors reduce to N-5 disconnected 1D topological branches corresponding to star polygons {N/q}.
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STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle
The paper proposes an STL-based optimization planner with uncertainty-aware risk analysis and event-triggered replanning for safe human-drone collaboration, demonstrated in simulations of an object handover task.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
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A Comparative Study of INDI and NDI with Nonlinear Disturbance Observer for Aerial Robotics
INDI exhibits stronger robustness than NDI+NDO under model mismatch, external disturbances, and measurement noise in simulation tests of aerial robot control.